System and method for analysis of a sample

ABSTRACT

A system including a light source, sampling tray, and a plurality of fiber optics positioned to achieve high contrast to improve accuracy and eliminate the need to rotate the sample. A composite light image from the fiber optics is fed to a spectrometer which converts the reflected light into a fingerprint corresponding to the concentration of at least one substance in the sample. The fingerprint is processed by a statistical model to determine concentration level of the at least one substance in the sample and the concentration level is then displayed.

This application is a continuation-in-part of U.S. patent applicationSer. No. 15/404,994 filed Jan. 12, 2017, now U.S. Pat. No. 9,952,233,which is a continuation of U.S. patent application Ser. No. 14/541,630filed Nov. 14, 2014, now U.S. Pat. No. 9,546,960. The entire disclosuresof these applications are incorporated herein by reference.

FIELD

This invention relates to the field of sample analysis and moreparticularly to an apparatus, system, and method for analyzingcomponents of a sample.

BACKGROUND

Analysis of samples, such as samples of cannabis-based plant matter,hops, animal feed, alfalfa, and agricultural products for humanconsumption, for content of certain substances is often required todetermine the usefulness, potency, effectiveness, and value of thesubstance. It is not generally possible to determine the content ofcertain substances and, hence, potential medicinal, nutritional,monetary, or other value of the substance, through color, taste, touch,or smell. Furthermore, it is desirable to determine the presence ofimpurities such as mold or pesticides in plant matter, especially ifusers are allergic to such.

Tetrahydrocannabinol (or THC), is the chemical responsible for most ofcannabis' (marijuana's) psychological effects. In general, the value ofa given quantity of a cannabis-based plant matter is somewhatproportional to the percentage of tetrahydrocannabinol in that cannabis.This is because the lower the THC content in the cannabis-based plantmatter, the more the user needs to consume to produce the desiredeffects. Likewise, the CBD (CannaBiDol) content in the cannabis-basedplant matter is significant because this component is known to havesignificant medical benefits. Another substance found in cannabis-basedplant matter is terpenes, also known as aromic terpenes. These compoundsgive the cannabis a unique smell. Terpenes are oily, volatile moleculesthat evaporate easily. Some 20,000 terpenes have been identified andcharacterized by their molecular structure, around 200 of which havebeen found in cannabis. Many terpenes have medicinal benefits such asAlpha-pinene (essential pine oil) which is often found in cannabis.Alpha-pinene is a bronchodilator potentially helpful for asthmatics.Pinene is also known to promote alertness and memory retention.

The percentage of CBD is a given amount of cannabis-based plant mattercan vary from almost zero (approximately 0.3% for some forms of hemp),but typically between 1 percent and 15 percent CBD (certain Charlotte'sWeb cannabis strains). So, if a consumer consumes a certain amount ofcannabis having 15% CBD, it would take approximately fifteen times thatamount of the cannabis-based plant matter having 1% CBD to achieve thesame desired medicinal effect and, therefore, the value of this range ofproduct varies with CBD concentration.

Given that there is no real way to determine the value of a given amountof cannabis-based plant matter by taste, smell, color, texture, etc., itis difficult for consumers and marketers to understand what they arebuying or selling. With alcohol, the consumer is notified of thepercentage of alcohol as a percentage or “proof” measurement that isprinted on the bottle such as 14.5% for a certain bottle of wine or 80proof (i.e., 40%) for a certain bottle of vodka.

Being that the CBD content of cannabis-based plant matter is determinedby many factors such as growing conditions (e.g., soil, light, water,fertilizer), plant lineage (e.g., genetic makeup), growing time, plantmaturity, drying, etc., there is a need to measure the many differentcompounds that are within a sample of cannabis-based plant matter.

What is needed is a portable system that will provide a direct value atleast one concentration of a substance in a sample of plant matter andother substances.

SUMMARY

In one embodiment, a portable sampling system is disclosed including aportable enclosure with a stabilized light source mounted in theportable enclosure for delivering light to a plant matter sample. Thesample is in communication with the stabilized light source and lightfrom the stabilized light source diffuse scattering from the sample.Pluralities of light collecting fiber optic cables are integrated insidethe portable enclosure and are specifically positioned to collect thereflected light from the sample so as to produce an image with thehighest possible contrast that eliminates sample surface roughnessimproving instrument accuracy. The plurality of fiber optics each add toa composite image which is sent to a spectrometer that receives thereflected light image from the fiber optics and converts the “composite”light image into an array of numeric values that represents afingerprint of substances within the sample.

In another embodiment, a method of measuring at least one substance in aplant-based sample is disclosed including for each sample of a pluralityof historical plant-based samples, collecting a fingerprint for the eachsample by exposing the each sample to a source of light, gatheringreflected light that has been reflected from the each sample from atleast two positions, transmitting the reflected light to a spectrometer,and generating the fingerprint from the reflected light by thespectrometer. Now, the concentration of at least one substance withineach sample is measured (e.g., sent to a laboratory for analysis,recording the result) and the fingerprint is associated with theconcentration of the at least one substance in a database. After severalfingerprints and associated concentration levels have been recorded, atleast one statistical model is created based on the fingerprints and theconcentration of at least one substance. Now to determine concentrationlevels of any of the at least one substance in an unknown sample ofplant-based matter, a new fingerprint is captured from the unknownsample of plant-based matter by exposing the unknown sample ofplant-based matter to the source of light, gathering reflected lightfrom at least two positions (the light reflected from the unknown sampleof plant-based matter), transmitting the reflected light to thespectrometer, and generating the new fingerprint from the reflectedlight by the spectrometer. Next, a concentration value for the at leastone substance in the unknown sample of plant-based matter is determinedby running the at least one statistical model against the newfingerprint and the concentration value for the at least one substancein the plant-based sample is displayed.

In another embodiment, a portable sampling system is disclosed includinga portable enclosure with a stabilized light source mounted in theportable enclosure for delivering light to a plant-based sample. Theplant-based sample is in communication with the stabilized light sourceand light from the stabilized light source is reflected from theplant-based sample. There are three fibers, each fiber being offset fromeach other so as to produce the highest contrast image improvinginstrument accuracy by eliminating effects of sample surface roughness,and each of the fibers collect the reflected light from the plant-basedsample. Each of three fiber optics is connected to supply a compositeimage and a spectrometer receives the reflected light and converts thecomposite light image into a fingerprint that represent concentrationsof substances in the cannabis-based sample.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention can be best understood by those having ordinary skill inthe art by reference to the following detailed description whenconsidered in conjunction with the accompanying drawings in which:

FIG. 1 illustrates a plan view of a system for analyzing samples ofplant matter.

FIG. 2 illustrates a plan view of an exemplary spectral analysis deviceof the system for analyzing samples of plant matter.

FIG. 3 illustrates a schematic view of a typical computer system withspectral analysis.

FIG. 4 illustrates a program flow of the system for generating astatistical model for the analysis of plant matter.

FIG. 5 illustrates a program flow of the system for analyzing samples ofplant matter using the statistical model.

DETAILED DESCRIPTION

Reference will now be made in detail to the presently preferredembodiments of the invention, examples of which are illustrated in theaccompanying drawings. Throughout the following detailed description,the same reference numerals refer to the same elements in all figures.

The herbaceous plant cannabis is grown, processed, and utilized in theUnited States under Government regulations and controlled by Governmentagencies, and in some states permitted for recreational and/or medicaluse. Research is often performed under the control of Federal Agencies,and evaluation as to the use of the drug as a therapeutic pharmaceuticalis being performed in the medical field. It is also known that cannabisis legally used as a euphoriant in certain geographic areas or states.

Referring to FIG. 1, a plan view of a system for analyzing samples ofplant matter is shown. In many scenarios, it is valuable to obtain ameasurement of the level of certain substances within a sample of plantmatter. For example, during the drying of the cannabis plant, and duringthe purchase/sale of a quantity of the plant or plant extract, it isvaluable to know the percentage of available substances such astetrahydrocannabinol (THC), tetrahydrocannabinolic acid (THCA),cannabidiol (CBD), cannabidiolic acid (CBDA), cannabinol (CBN),moisture, mold, and to a lesser extent, protein, fat, etc. Further, formedicinal reasons, it is valuable to know the concentration of variousterpenes, for example Alpha-pinene, Myrcene, Limonene, Linalool, andBeta-caryophyllene.

As an example, purchasing 10 g of cannabis (assuming all percentages ofother components are equal) having a 10% moisture content is better thanpurchasing 10 g of cannabis-based plant matter having a 20% moisturecontent, being that since you are purchasing by weight, you are payingfor the weight of the water in the product you are buying.

To understand concentrations of various substances within a sample ofplant matter prior to the present invention, a sample of the plantmatter needed to be shipped to a laboratory for full analysis by gaschromatography or by liquid chromatography, but several issues arisefrom doing such. First, the round trip to the laboratory takes time andthe opportunity for purchase/sale may wane while waiting for results.Second, the plant matter will change characteristics and content duringthe shipping to the laboratory. For example, moisture evaporates andmold grows/increases. Third, the cost of gas chromatography and liquidchromatography analysis is often very high, and fourth, due toindividual laws of certain locations, the transport of certainplant-based substances such as cannabis outside of certain areas isoften illegal. Therefore, there is a significant advantage to performingfield analysis on many different types of plant matter.

As described, in laboratory analysis of plant matter, performance a fullgas chromatography or liquid chromatography analysis on a sample of theplant matter is required for each component (e.g., one analysis for THC,another for CBD, etc.). The output of each analysis is a listing of allcomponent substances of the sample plant matter based on GCstandards/methods/procedures or LC standards/methods/procedures whichare different even between different Labs. Often, a few days arerequired to perform this analysis, plus shipping time.

Instead, there is an advantage in comparing to hundreds of previouslymeasured samples of plant matter and producing a value that is relativeto each of the prior samples instead of providing the grower, seller,buyer, or user with data that can vary between laboratories performingthe testing. The disclosed system does such, by capturing numericalarrays (i.e., fingerprints of molecules) of measurements related to eachof a set of plant matter and associating those numerical arrays with anactual analysis of the targeted substances in each of the set of plantmatter, for example, by gas chromatography or by liquid chromatography.From this set of data, a statistical model is built. Then, when a newfingerprint (numerical array) for an unknown sample of plant matter iscaptured, this statistical model is used to evaluate the unknown sampleof plant matter and report values for specific compounds. In asimplistic example in which only THC content of cannabis-based plantmatter is of interest, assume ten spectrographic samples are collectedfrom ten different samples of cannabis-based plant matter, each samplehaving a fingerprint (spectral chart) and an associated THC contentvalue that is obtained by other means (e.g., by gas chromatography, byliquid chromatography, using “statistically confirmed” referencestandards and procedures). From these ten fingerprints and associatedTHC content values, a statistical model is built using, for example,available MATLAB software programs that applies weighting factors tonumerical array fingerprints that correctly yields the known THCconcentration for each future sample. There are many math packages knownthat will produce a statistical model given a set of data (e.g. a set offingerprints) using, for example, MATLAB functions for chemometriccalibration. Such packages are well known from various companies such asMathWorks®, Camo, InfoMetrix®, etc. These packages are intended for usewith analytical instruments in the analysis of large datasets.

Now, for example, when a new, unknown sample of cannabis-based plantmatter is spectrally measured yielding a new fingerprint (spectralarray), this fingerprint of cannabis-based plant matter is thenprocessed by the statistical model and a THC value of the unknown sampleof cannabis-based plant matter is determined, then stored, printed,and/or displayed. Again, THC is used in this description as an exampleand any of the possible substances available in cannabis-based plantmatter are modeled and later analyzed in the same manner, for example,tetrahydrocannabinol (THC), tetrahydrocannabinolic acid (THCA),cannabidiol (CBD), cannabidiolic acid (CBDA), cannabinol (CBN),moisture, mold, protein, fat, terpenes (e.g., Alpha-pinene, Myrcene,Limonene, Linalool, and Beta-caryophyllene), etc.

In the exemplary plant matter sampling system 10 of FIG. 1, multipleangled sides 13 are aimed toward the top surface 11 on which thesampling cup 14 rests. The plant matter sample 16 is placed in thesampling cup 14 which is in optical communication with a light source12. Light from the light source 12 is directed onto the sample 16 andreflects off the sample 16 onto a plurality of receptors 20/22/24 forgathering light. Each of the receptors 20/22/24 are positioned on one ofthe angled sides 13 of the cannabis-based plant matter sampling system10. The light then travels through the fiber optics 30/32/34 to aspectrometer 50 (e.g. an infrared spectrometer 50). The spectrometer 50creates a multi-dimensional image of the light from the plurality ofreceptors 20/22/24. Although many different light sources 12 areanticipated with narrow or wide spectrums of emitted light, it ispreferred that the light source 12 emit light in the range of 900 to2300 nanometers.

The three-dimensional image of the light (i.e., a fingerprint of theplant material) is then transferred to a computer system 70 through, forexample, a serial interface 52 such as a USB interface 52, though anyknown or future interface 52 is anticipated. In some embodiments, theinterface 52, and hence the computer system 70, provides power to thelight source 12, while in other embodiments, power is provided for thelight source in other ways such as from a power supply, battery, solarpanel, etc. The computer system 70 has at least a display 86 fordisplaying results of the analysis and some form of storage 88 forstoring software and models required to analyze each sampling. It isestimated that the analysis of a sample is possible within 30 secondswith around a 1% accuracy.

In contrast to general-purpose, large and expensive gas chromatographyor liquid chromatography systems, the disclosed invention providesdirect readings of key compounds typically found in plant matter, suchas tetrahydrocannabinol (THC), tetrahydrocannabinolic acid (THCA),cannabidiol (CBD), cannabidiolic acid (CBDA), cannabinol (CBN),terpenes, moisture, and mold. For example, instead of presenting afrequency vs molecular absorption chart of substance concentrations, alist of percentages is displayed on the display 86 of the computersystem 70. For example, if 3% THC, 21% moisture, and 1% mold ismeasured, then a message as: “3% THC, 21% H2O, 1% mold” is displayed onthe display 86 of the computer system 70, this data being what isimportant to the grower, store owner, and the consumer.

In some embodiments, to support the plant matter sampling system 10, afixture 18 is included having a substantially flat bottom for supportingthe plant matter sampling system 10 on, for example, a table or othersurface.

FIG. 2, a plan view of an exemplary spectral analysis device 50 isshown. In this, the light that is reflected from the plant matter sample16 is received at multiple angles (three angles in this example thoughany number of angles is anticipated). The light is transmitted throughthe fiber optic tubes 30/32/34 (again, three in this example), and thelight is aimed at one or more filters 56 by one or more lenses 55,resulting in the various elements of a photo detection array 57 beingilluminated at individual intensities proportional to certaincharacteristics of the cannabis sample 16. Although many photo detectionarrays 57 are anticipated, examples of a photo detection array 57include a photo-diode array 57 and/or what is known as a charge-coupleddevice 57 often called a CCD 57. In general, the photo detection array57 receives an intensity of light at each element dependent upon thecomposition of various chemicals in the sample 16. The photo detectionarray 57 converts each light intensity to an electrical value that isdetected by logic 58 and converted into, for example, an array ofdigital values (i.e., the fingerprint of the plant matter sample 16)that is then transmitted to the computer system 70 (see FIG. 1) over aninterface 52, for example, a USB interface 52. This array of digitalvalues represents the fingerprint of the current plant matter sample 16.

Referring to FIG. 3, a schematic view of a typical computer system withspectral analysis 70 is shown. The exemplary computer system withspectral analysis 70 utilizes any known processor-based system. Thisexemplary computer system with spectral analysis 70 is shown in itssimplest form. Different architectures are known that accomplish similarresults in a similar fashion and the present invention is not limited inany way to any particular computer system with spectral analysis 70architecture or implementation. In this exemplary computer system withspectral analysis 70, a processor 71 executes or runs programs from amemory 74. The programs that measure the sample 16 are generally loadedinto the random access memory 74 when needed. The processor 71 is anyknown or future processor, typically a processor 71 as typically used innotebook computers, tablet computers, cellular phones, and the like. Therandom access memory 74 is typically connected to the processor 71 by,for example, a memory bus 72. The random access memory 74 is any memory74 suitable for connection and operation with the selected processor 71,such as SRAM, DRAM, SDRAM, RDRAM, DDR, DDR-2, etc.

Also connected to the processor 71 is a system bus 82 for interfacingwith peripheral subsystems such as a network interface 80 and a graphicsadapter 84. The graphics adapter 84 receives commands from the processor70 and controls what is depicted on a display image on the display 86,including status messages and analysis output.

In general, persistent storage 88 stores operating procedures, controldata, programs, etc., as known in the industry. It is anticipated thatalgorithms and statistical models that are used to calculate valuesrelated to the sample 16 are stored in the persistent storage 88.

The peripherals shown are examples and other peripherals are known inthe industry such as speakers, microphones, USB interfaces, Bluetoothtransceivers, Wi-Fi transceivers, touch screen inputs, image sensors,temperature sensors, etc., the likes of which are not shown for brevityand clarity reasons.

The network interface 80 connects the computer system with spectralanalysis 70 to other systems for various purposes such as uploading newfingerprints along with measured contents and downloading of models andupdated models, etc.

The computer system with spectral analysis 70 communicates with thespectral analysis subsystem 50 through any known communication system,for example, as shown, a USB port 51 and a USB connection 52. Manydifferent communication systems are anticipated and the simplified USBport is one example as using any form of data communication to thespectral analysis subsystem 50, including, but not limited to any of thevarious known digital and analog interfaces such as NRZ (non-return tozero), RS-232, I2C, IIC, etc. In some embodiments, a wireless interfacesuch as near-filed or Bluetooth is used.

The computer system with spectral analysis 70 typically has one or moreuser controls 93 such as touch screen inputs, keyboards, joysticks,mice, etc., through which the operation of the disclosed applicationsand algorithms are initiated and controlled.

Referring to FIG. 4, a program flow of the system for analyzing knownsamples of plant matter and generating a statistical model of suchsamples is shown. This is an example of one such program flow thatoperates in the computer system 70 with interaction with the plantmatter sampling system 10 to present a direct reading of one or morecontents of the plant matter sample 16.

Although the description below shows data gathering operations using onesystem for analyzing known samples of plant matter, it is fullyanticipated that fingerprints and data are collected from many similarsystems for analyzing known samples of plant matter and that each set offingerprints and data contribute further to the accuracy of thedeveloped statistical models.

The creation of the statistical model requires multiple plant mattersamples 16 that have been or are to be analyzed by other means (e.g., bygas chromatography or by liquid chromatography). The exemplary flowbegins with preparation and possible self-tests to determine if theplant matter sampling system 10 is ready to use 100. Once the plantmatter sampling system 10 is ready 100, the light source 12 is energized102 to emit light, for example, a broad spectrum of light is emitted inthe range of 900 to 2300 nanometers. The light is directed at the plantmatter sample 16 and some of the light is reflected and received intothe plurality of fiber optics 30/32/34 at a plurality of angles. Forexample, three such angles and fiber optics 30/32/34 are shown in FIGS.1 and 2. The received light is transmitted to the spectrometer 50, whichis any spectrometer 50. In this exemplary spectrometer 50, the light isrefracted and aimed at a detector array 57/58 where the variousintensities of light at each element of the detector array 57/58 isconverted into an electrical signal, then into a numeric value,resulting in an array of numeric values that represents a fingerprint ofthe composition of the cannabis-based plant matter sample 16.

Once the array of numeric values is received 104, in some embodiments,the light source 12 is shut off 110 and the array of number values issaved 112. In this, the numeric array of values is used as a fingerprintand is associated with an actual set of measured values that areindependently derived from the sample of cannabis-based plant matter 16(e.g., measured by gas chromatography or by liquid chromatography).

Next, it is determined if enough fingerprints and analyzed results havebeen collected 114 and, if not, the above steps are repeated to obtainmore fingerprints and analyzed results. Once sufficient fingerprintshave been collected 114, statistical models are generated 120 based uponthis set of fingerprints and the associated values of various componentsreported from analysis of each sample of plant matter 16. The resultingstatistical models are then distributed 122 to one or more systems foranalyzing samples of plant.

Note, as further fingerprints are captured from new samples of plantmatter 16, along with analysis of such, the above steps are repeated togenerate updated statistical models, typically having greater accuracy,and these statistical models are then distributed 122 to one or moresystems for analyzing samples of plant matter.

Referring to FIG. 5, a program flow of the system for analyzing samplesof plant matter is shown. This is an example of one such program flowthat operates in the computer system 70 with interaction with the plantmatter sampling system 10 to present a direct reading of one or morecontents of the plant matter sample 16.

The exemplary flow begins with preparation and possible self-tests todetermine if the plant matter sampling system 10 is ready to use 200.Once the plant matter sampling system 10 is ready 200, the light source12 is energized 202 to emit light, for example, a broad spectrum oflight is emitted in the range of 900 to 2300 nanometers. The light isdirected at the plant matter sample 16 and some of the light isreflected and received into the plurality of fiber optics 30/32/34 at aplurality of angles. For example, three such angles and three fiberoptics 30/32/34 are shown in FIGS. 1 and 2. The received light istransmitted to the spectrometer 50, which is any spectrometer 50. Inthis exemplary spectrometer 50, the light is refracted and aimed at adetector array 57/58 where the various intensities of light at eachelement of the detector array 57/58 is converted into an electricalsignal, then into a numeric value, resulting in an array of numericvalues that represents a fingerprint of the composition of the plantmatter sample 16.

Once the array of numeric values (fingerprint) is captured 204, thelight source 12 is optionally shut off 210 and the fingerprint isprocessed 212 by the one or more statistical models previously generated(see FIG. 4). The statistical model(s) determine a concentration (orpresence) of one or more specific substances within the plant mattersample 16. In this, the fingerprint is processed by one or morestatistical models (e.g., a model that was generated as in FIG. 4) todetermine the values of the specific substances of the sample 16. Oneexemplary statistical model is a curve fitting algorithm such as PartialLeast Square (PLS) algorithms to find where the array of number values(fingerprint) for the sample 16 fits within a statistical modelgenerated using the plurality of previous known samples of the plantmatter (as in FIG. 4). From this statistical model 212, numeric valuesfor one or more targeted substances of the plant matter sample 16 iscalculated. Preferably, the numeric values for each targeted substancesis then displayed 214, for example on the display 86 (see FIGS. 1 and3). For example, one exemplary numerical value that can be displayed isthe percentage of tetrahydrocannabinol (THC) present in a cannabis-basedplant matter sample (e.g., “The sample contains 2.9% THC”).

Again, as more and more new samples of the plant matter are capturedusing this methods of FIG. 4, the database is improved with the newsamples and updated statistical models are created from the database anddistributed to one or more of the plant matter sampling systems 10,thereby improving the accuracy of each the plant matter sampling system10.

The plant matter sampling system 10 measures any forms or classes ofplant matter and/or products containing plant matter, typically dried orprocessed, including plant form (leaves, buds, etc.), extracts, waxes,oils, edible products containing plant matter, etc. It is fullyanticipated that, for each class of plant matter, a differentstatistical model is created and provided for use in measuring samples16 of that class of plant matter. For example, to measure the amount ofTHC in a cookie containing cannabis, a piece of the cookie that containscannabis is placed in the sampling dish 14 and the above process 200-214is performed using a statistical model that was generated using knownsamples of that class of cannabis-based cookies. This statistical modelwas created by measuring several known samples of this class ofcannabis-based products as above and capturing the numeric array thatrepresents this sample (fingerprint), then analyzing this sample in thelaboratory and associating the results of this analysis with the numericarray. From this, the statistical model for cookies containing cannabisis generated and distributed to other cannabis-based plant mattersampling systems 10.

In order to develop larger libraries of known samples, it is anticipatedthat the manufacturer of the plant matter sampling system 10 willrequest users of existing plant matter sampling systems 10 to providenumeric arrays (i.e., fingerprints) of samples along with the laboratoryanalysis of the samples to the manufacturer for integration intodatabases and for generation of improved statistical models. Forexample, after receiving 20 samples, each having the numeric array(fingerprint) and laboratory measurement data, the manufacturer of theplant matter sampling systems 10 provides an additional month rental orother cash incentives.

Equivalent elements can be substituted for the ones set forth above suchthat they perform in substantially the same manner in substantially thesame way for achieving substantially the same result.

It is believed that the system and method as described and many of itsattendant advantages will be understood by the foregoing description. Itis also believed that it will be apparent that various changes may bemade in the form, construction and arrangement of the components thereofwithout departing from the scope and spirit of the invention or withoutsacrificing all of its material advantages. The form herein beforedescribed being merely exemplary and explanatory embodiment thereof. Itis the intention of the following claims to encompass and include suchchanges.

What is claimed is:
 1. A portable spectrometer system for analysis of aplant-based sample, comprising: a portable enclosure; a stabilized lightsource mounted in the portable enclosure for delivering light to aplant-based sample, and light from the stabilized light source reflectsfrom the plant-based sample; a plurality of fiber optics, eachpositioned to capture the reflected light from the plant-based samplethen combined to produce a composite image with high contrast improvingaccuracy by eliminating errors due to sample surface roughness and theneed for rotating the plant-based sample, said plurality of fiber opticseach being arranged to receive light from the plant-based sample at adifferent angle than a second angle at which another of said pluralityof fiber optics receives light, said angles being selected to enhanceoptical image contrast of said composite image; and a spectrometerconfigured to receive the high contrast image of reflected light fromthe fiber optics and convert the light image into an array of numericvalues that represents a fingerprint of substances within theplant-based sample achieving the best accuracy without moving parts. 2.The portable spectrometer system of claim 1, wherein the plant-basedsample is cannabis-based and wherein the one or more targeted substancesof the cannabis-based sample is selected from a set of substancescomprising tetrahydrocannabinol (THC), tetrahydrocannabinolic acid(THCA), cannabidiol (CBD), cannabidiolic acid (CBDA), cannabinol (CBN),moisture, and mold.
 3. A method of measuring at least one substance in aplant-based sample, the method comprising: for each sample of aplurality of historical plant-based samples: collecting a fingerprintfor each sample by exposing each sample to a source of light, gatheringreflected light from at least two angles, the reflected light havingbeen reflected from each sample, transmitting the reflected light to aspectrometer, and generating the fingerprint from the reflected light bythe spectrometer; measuring the concentration of at least one substancewithin each sample; associating the fingerprint with the concentrationof the at least one substance in a database; generating at least onestatistical model based on the fingerprint and the concentration of atleast one substance; for each unknown sample of plant-based matter:collecting a new fingerprint for the unknown sample of plant-basedmatter by exposing the unknown sample of plant-based matter to thesource of light, gathering reflected light from at least three angles,the reflected light having been reflected from the unknown sample ofplant-based matter, transmitting the reflected light to thespectrometer, and generating the new fingerprint from the reflectedlight by the spectrometer; determining a concentration value for the atleast one substance in the unknown sample of plant-based matter byrunning the at least one statistical model against the new fingerprint;displaying the concentration value for the at least one substance in theplant-based sample.
 4. The method of claim 3, wherein the at least onestatistical model is a Partial Least Square (PLS) algorithm.
 5. Themethod of claim 3, wherein the at least one substance in the plant-basedsample is selected from a set of substances comprisingtetrahydrocannabinol (THC), tetrahydrocannabinolic acid (THCA),cannabidiol (CBD), cannabidiolic acid (CBDA), cannabinol (CBN),moisture, and mold.
 6. The method of claim 3, wherein the at least onesubstance in the plant-based sample is selected from available terpenesselected from the set of terpenes comprising Alpha-pinene, Myrcene,Limonene, Linalool, and Beta-caryophyllene.
 7. The method of claim 3,wherein the source of light emits light at wavelengths in the range of900 to 2300 nanometers.